{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import *\n",
    "from collections import Counter\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "IMAGE_DIR = 'image_contest_level_2'\n",
    "CROP_DIR = 'crop_split'\n",
    "\n",
    "from multiprocessing import Pool, Lock, Manager"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据并行预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def f(index):\n",
    "    global x, bw\n",
    "    img = cv2.imread('%s/%d.png'%(IMAGE_DIR, index))\n",
    "    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "    eq = cv2.equalizeHist(gray)\n",
    "    b = cv2.medianBlur(eq, 9)\n",
    "    \n",
    "    m, n = img.shape[:2]\n",
    "    b2 = cv2.resize(b, (n//4, m//4))\n",
    "\n",
    "    m1 = cv2.morphologyEx(b2, cv2.MORPH_OPEN, np.ones((7, 40)))\n",
    "    m2 = cv2.morphologyEx(m1, cv2.MORPH_CLOSE, np.ones((4, 4)))\n",
    "    _, bw = cv2.threshold(m2, 127, 255, cv2.THRESH_BINARY_INV)\n",
    "    \n",
    "    bw = cv2.resize(bw, (n, m))\n",
    "\n",
    "    img2, ctrs, hier = cv2.findContours(bw, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
    "    if len(ctrs) > 3:\n",
    "        print index\n",
    "    \n",
    "    # 微调三个公式\n",
    "    d = 10\n",
    "    imgs = []\n",
    "    sizes = []\n",
    "    for i, ctr in enumerate(ctrs):\n",
    "        x, y, w, h = cv2.boundingRect(ctr)\n",
    "        if w*h < 1000:\n",
    "            continue\n",
    "        roi = img[max(0, y-d):min(m, y+h+d),max(0, x-d):min(n, x+w+d)]\n",
    "        \n",
    "        x = b[max(0, y-d):min(m, y+h+d),max(0, x-d):min(n, x+w+d)]\n",
    "        _, x = cv2.threshold(x, 127, 255, cv2.THRESH_BINARY_INV)\n",
    "        _, x, _ = cv2.findContours(x, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
    "        x, y, w, h = cv2.boundingRect(np.vstack(x))\n",
    "        roi2 = roi[y:y+h,x:x+w]\n",
    "        imgs.append(roi2)\n",
    "        cv2.imwrite('%s/%d_%d.png'%(CROP_DIR, index, i), roi2)\n",
    "        sizes.append((w, h))\n",
    "    \n",
    "    # 连接三个公式\n",
    "    sizes = np.array(sizes)\n",
    "    w, h = sizes[:,1].max(), sizes[:,0].sum()+(len(sizes)-1)*2\n",
    "    img = np.zeros((w, h, 3), dtype=np.uint8)\n",
    "    x = 0\n",
    "    for a in imgs[::-1]:\n",
    "        iw = a.shape[1]\n",
    "        img[:a.shape[0], x:x+iw] = a\n",
    "        x += iw + 2\n",
    "    \n",
    "    cv2.imwrite('%s/%d.png'%(CROP_DIR, index), img)\n",
    "    \n",
    "    return [index, len(sizes)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100000/100000 [23:57<00:00, 69.58it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 20.1 s, sys: 3.34 s, total: 23.4 s\n",
      "Wall time: 23min 57s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "try:\n",
    "    p\n",
    "except:\n",
    "    p = Pool(12)\n",
    "\n",
    "n = 100000\n",
    "if __name__ == '__main__':\n",
    "    rs = []\n",
    "    for r in tqdm(p.imap_unordered(f, range(n)), total=n):\n",
    "        rs.append(r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>r</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   r\n",
       "i   \n",
       "0  3\n",
       "1  3\n",
       "2  2\n",
       "3  2\n",
       "4  3"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(rs, columns=list('ir'))\n",
    "df.index = df['i']\n",
    "df = df.sort_index()\n",
    "df = df.drop('i', 1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>r</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [r]\n",
       "Index: []"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['r'] > 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.to_csv('r.csv', index=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import struct\n",
    "import imghdr\n",
    "\n",
    "def get_image_size(fname):\n",
    "    '''Determine the image type of fhandle and return its size.\n",
    "    from draco'''\n",
    "    with open(fname, 'rb') as fhandle:\n",
    "        head = fhandle.read(24)\n",
    "        if len(head) != 24:\n",
    "            return\n",
    "        if imghdr.what(fname) == 'png':\n",
    "            check = struct.unpack('>i', head[4:8])[0]\n",
    "            if check != 0x0d0a1a0a:\n",
    "                return\n",
    "            width, height = struct.unpack('>ii', head[16:24])\n",
    "        elif imghdr.what(fname) == 'gif':\n",
    "            width, height = struct.unpack('<HH', head[6:10])\n",
    "        elif imghdr.what(fname) == 'jpeg':\n",
    "            try:\n",
    "                fhandle.seek(0) # Read 0xff next\n",
    "                size = 2\n",
    "                ftype = 0\n",
    "                while not 0xc0 <= ftype <= 0xcf:\n",
    "                    fhandle.seek(size, 1)\n",
    "                    byte = fhandle.read(1)\n",
    "                    while ord(byte) == 0xff:\n",
    "                        byte = fhandle.read(1)\n",
    "                    ftype = ord(byte)\n",
    "                    size = struct.unpack('>H', fhandle.read(2))[0] - 2\n",
    "                # We are at a SOFn block\n",
    "                fhandle.seek(1, 1)  # Skip `precision' byte.\n",
    "                height, width = struct.unpack('>HH', fhandle.read(4))\n",
    "            except Exception: #IGNORE:W0703\n",
    "                return\n",
    "        else:\n",
    "            return\n",
    "        return width, height"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100000/100000 [00:04<00:00, 23918.74it/s]\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('size.csv')\n",
    "sizes = []\n",
    "fnames = []\n",
    "for i in tqdm(range(100000)):\n",
    "    w = 0\n",
    "    h = 0\n",
    "    for j in range(df['r'][i]):\n",
    "        fname = '%s/%d_%d.png'%(CROP_DIR, i, j)\n",
    "        fnames.append(fname)\n",
    "        size = get_image_size(fname)\n",
    "        w += size[0]\n",
    "        h = max(h, size[1])\n",
    "    sizes.append((w, h))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wmin wmax hmin hmax\n",
      "516 861 46 72\n"
     ]
    }
   ],
   "source": [
    "s = np.array(sizes)\n",
    "print 'wmin wmax hmin hmax'\n",
    "print s[:,0].min(), s[:,0].max(), s[:,1].min(), s[:,1].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 结果可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def disp2(img):\n",
    "    cv2.imwrite('a.png', img)\n",
    "    return Image('a.png')\n",
    "\n",
    "\n",
    "def disp(img, txt=None, first=False):\n",
    "    global index\n",
    "    if first:\n",
    "        index = 1\n",
    "        plt.figure(figsize=(16, 9))\n",
    "    else:\n",
    "        index += 1\n",
    "    plt.subplot(4, 3, index)\n",
    "    if len(img.shape) == 2:\n",
    "        plt.imshow(img, cmap='gray')\n",
    "    else:\n",
    "        plt.imshow(img[:,:,::-1])\n",
    "    if txt:\n",
    "        plt.title(txt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 技术原理\n",
    "\n",
    "* [转灰度图](http://docs.opencv.org/master/df/d9d/tutorial_py_colorspaces.html)\n",
    "* [二值化](http://docs.opencv.org/master/d7/d4d/tutorial_py_thresholding.html)\n",
    "* [直方图均衡](http://docs.opencv.org/master/d5/daf/tutorial_py_histogram_equalization.html)\n",
    "* [中值滤波](http://docs.opencv.org/master/d4/d13/tutorial_py_filtering.html)\n",
    "* [开运算](http://docs.opencv.org/master/d9/d61/tutorial_py_morphological_ops.html)\n",
    "* [轮廓查找](http://docs.opencv.org/master/d4/d73/tutorial_py_contours_begin.html)\n",
    "* [边界矩形](http://docs.opencv.org/master/dd/d49/tutorial_py_contour_features.html)\n",
    "\n",
    "参考链接：\n",
    "\n",
    "* http://docs.opencv.org/master/df/d9d/tutorial_py_colorspaces.html\n",
    "* http://docs.opencv.org/master/d7/d4d/tutorial_py_thresholding.html\n",
    "* http://docs.opencv.org/master/d5/daf/tutorial_py_histogram_equalization.html\n",
    "* http://docs.opencv.org/master/d4/d13/tutorial_py_filtering.html\n",
    "* http://docs.opencv.org/master/d9/d61/tutorial_py_morphological_ops.html\n",
    "* http://docs.opencv.org/master/d4/d73/tutorial_py_contours_begin.html\n",
    "* http://docs.opencv.org/master/dd/d49/tutorial_py_contour_features.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1lc1XslEYbH/U4fMAQMwTl3A85mmGd2A9Dks34T7S8q2xy7+Ud28CmCxLgakj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\neIQFjEWo+Tz/A5CNYj90daFPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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y5LSpufK3p+fO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\n4iSpJEmSJEmSJEmSpIry/9Ny20oLvEnAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e8cf79a50>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 932
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot(index):\n",
    "    global img, gray, b, eq, bw, m1, m2, r, roi\n",
    "    img = cv2.imread('%s/%d.png'%(IMAGE_DIR, index))\n",
    "    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "    eq = cv2.equalizeHist(gray)\n",
    "    b = cv2.medianBlur(eq, 11)\n",
    "    \n",
    "    m, n = img.shape[:2]\n",
    "    b2 = cv2.resize(b, (n//4, m//4))\n",
    "\n",
    "    m1 = cv2.morphologyEx(b2, cv2.MORPH_OPEN, np.ones((7, 40)))\n",
    "    m2 = cv2.morphologyEx(m1, cv2.MORPH_CLOSE, np.ones((4, 4)))\n",
    "    _, bw = cv2.threshold(m2, 127, 255, cv2.THRESH_BINARY_INV)\n",
    "    \n",
    "    bw = cv2.resize(bw, (n, m))\n",
    "\n",
    "    r = img.copy()\n",
    "    img2, ctrs, hier = cv2.findContours(bw, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
    "    for ctr in ctrs:\n",
    "        x, y, w, h = cv2.boundingRect(ctr)\n",
    "        cv2.rectangle(r, (x, y), (x+w, y+h), (0, 255, 0), 10)\n",
    "    x, y, w, h = cv2.boundingRect(np.vstack(ctrs))\n",
    "    \n",
    "    disp(img, 'raw img', 1)\n",
    "    disp(eq, 'eq')\n",
    "    disp(b, 'blur')\n",
    "    disp(m1, 'm1')\n",
    "    disp(m2, 'm2')\n",
    "    disp(r, 'rect')\n",
    "    \n",
    "    # 微调三个公式\n",
    "    d = 10\n",
    "    imgs = []\n",
    "    sizes = []\n",
    "    for i, ctr in enumerate(ctrs):\n",
    "        x, y, w, h = cv2.boundingRect(ctr)\n",
    "        roi = img[max(0, y-d):min(m, y+h+d),max(0, x-d):min(n, x+w+d)]\n",
    "        \n",
    "        x = b[max(0, y-d):min(m, y+h+d),max(0, x-d):min(n, x+w+d)]\n",
    "        _, x = cv2.threshold(x, 127, 255, cv2.THRESH_BINARY_INV)\n",
    "        _, x, _ = cv2.findContours(x, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
    "        x, y, w, h = cv2.boundingRect(np.vstack(x))\n",
    "        roi2 = roi[y:y+h,x:x+w]\n",
    "        imgs.append(roi2)\n",
    "        sizes.append((w, h))\n",
    "    \n",
    "    # 连接三个公式\n",
    "    sizes = np.array(sizes)\n",
    "    img = np.zeros((sizes[:,1].max(), sizes[:,0].sum()+len(sizes)-1, 3), dtype=np.uint8)\n",
    "    x = 0\n",
    "    for a in imgs[::-1]:\n",
    "        w = a.shape[1]\n",
    "        img[:a.shape[0], x:x+w] = a\n",
    "        x += w + 1\n",
    "    \n",
    "    return disp2(img)\n",
    "    \n",
    "plot(56044)"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": []
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